Improved winter road condition classification method for autonomous driving based on hierarchical transformer
DOI: 10.23977/acss.2024.080620 | Downloads: 14 | Views: 294
Author(s)
Zhengguang Lu 1, Huaqi Zhao 1
Affiliation(s)
1 School of Information and Electronic Technology, Jiamusi University, Jiamusi, China
Corresponding Author
Huaqi ZhaoABSTRACT
Aiming at the problem that the existing road surface classification methods cannot accurately identify the conditions of winter road surface with low discrimination, we propose an improved winter road condition classification method based on hierarchical Transformer. Firstly, dynamic overlapping patch embedding is introduced, which can flexibly handle input features of any size and preserve the continuity of local detail information through dynamic position encoding and overlapping patch embedding. Then, frequency-based factorized attention is used to extract frequency features containing high-level context information, enhancing the feature representation between image categories. Finally, a multi-level feature fusion method based on weight average strategy is proposed, by evenly allocating the dynamically upgradable weights to the output layers of each stage and performing multi-level fusion, the low-level features are projected to the high-level to continue learning, the feature representation is enriched, and the discrimination of the classification image is increased, thereby the classification performance is improved. Experiments are carried out on the WRF dataset of snow and ice road. The classification accuracy of the proposed method can reach 88.93% with only 3.8M parameters and 0.6G computational complexity, which is better than the current mainstream road classification methods.
KEYWORDS
Transformer, Classification, Dynamic overlapping patch embedding, Frequency-based factorized attention, Multi-level feature fusionCITE THIS PAPER
Zhengguang Lu, Huaqi Zhao, Improved winter road condition classification method for autonomous driving based on hierarchical transformer. Advances in Computer, Signals and Systems (2024) Vol. 8: 159-166. DOI: http://dx.doi.org/10.23977/acss.2024.080620.
REFERENCES
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